Surgical Treatment of Chronic Hand Ischemia: A Systematic Review and Case Series
Bibliographic record
Abstract
Background: Chronic hand ischemia refers to progressive, non-acute ischemic symptoms such as cold intolerance, rest pain, ulceration, tissue necrosis, and digit loss and poses a significant challenge in management. Conservative treatment begins with medical optimization and pharmacologic therapy, but when symptoms persist, surgical intervention may be required. Various operations exist to improve circulation including sympathectomy, arterial bypass, or venous arterialization. The purpose of this study is to systematically review published outcomes and present our experience with each surgical technique. Methods: A systematic review of literature regarding surgical treatment of chronic hand ischemia published between 1990 and 2016 was conducted using PRISMA guidelines. A retrospective-review of surgical interventions for chronic hand ischemia from 2010 to 2016 was then conducted. Primary outcomes included improvement in pain, wound-healing, and development of new ulcerations. Results: The review included 38 eight studies, showing all three techniques were effective in treating chronic hand ischemia. Sympathectomy had the lowest rate of new ulcerations (0.8%); bypass had the highest rate of healing existing ulcerations (89%). Arterialization was associated with consistent pain improvement pain (100%) but more complications (30.8%). Our series included 18 patients with 21 affected hands, 18 sympathectomies, 6 ulnar artery bypasses, and 1 arterialization. Most hands had improvement of wounds (89.5%) and pain (78.9%). No patients developed new ulcerations, but one required secondary amputation. Conclusions: When conservative measures fail to improve chronic hand ischemia, surgical intervention is an effective last line treatment. An algorithmic approach can determine the best operation for patients with chronic hand ischemia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".